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A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.
| Date | Stars |
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| 2026-07-24 | 14751 |
| 2026-07-25 | 14759 |
| 2026-07-28 | 14759 |
| 2026-07-30 | 14759 |
| 2026-08-06 | 14759 |
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# OpenMythos
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> **Disclaimer:** OpenMythos is an independent, community-driven theoretical reconstruction based solely on publicly available research and speculation. It is not affiliated with, endorsed by, or connected to Anthropic or any of their proprietary systems.
OpenMythos is an open-source, theoretical implementation of the Claude Mythos model. It implements a Recurrent-Depth Transformer (RDT) with three stages: **Prelude** (transformer blocks), a looped **Recurrent Block** (up to `max_loop_iters`), and a final **Coda**. Attention is switchable between MLA and GQA, and the feed-forward uses a sparse MoE with routed and shared experts ideal for exploring compute-adaptive, depth-variable reasoning.
## Installation
```bash
pip install open-mythos
#uv pip install open-mythos
```
To enable Flash Attention 2 in `GQAttention` (requires CUDA and build tools):
```bash
pip install open-mythos[flash]
```
## Usage
```python
import torch
from open_mythos.main import OpenMythos, MythosConfig
attn_type = "mla" # or "gqa"
base = {
"vocab_size": 1000,
"dim": 256,
"n_heads": 8,
"max_seq_len": 128,
"max_loop_iters": 4,
"prelude_layers": 1,
"coda_layers": 1,
"n_experts": 8,
"n_shared_experts": 1,
"n_experts_per_tok": 2,
"expert_dim": 64,
"lora_rank": 8,
"attn_type": attn_type,
}
if attn_type == "gqa":
cfg = MythosConfig(**base, n_kv_heads=2)
else:
cfg = MythosConfig(
**base,
n_kv_heads=8,
kv_lora_rank=32,
q_lora_rank=64,
qk_rope_head_dim=16,
qk_nope_head_dim=16,
v_head_dim=16,
)
model = OpenMythos(cfg)
total = sum(p.numel() for p in model.parameters())
print(f"\n[{attn_type.upper()}] Parameters: {total:,}")
ids = torch.randint(0, cfg.vocab_size, (2, 16))
logits = model(ids, n_loops=4)
print(f"[{attn_type.upper()}] Logits shape: {logits.shape}")
out = model.generate(ids, max_new_tokens=8, n_loops=8)
print(f"[{attn_type.upper()}] Generated shape: {out.shape}")
A = model.recurrent.injection.get_A()
rho = torch.linalg.eigvals(A).abs().max().item()
print(
f"[{attn_type.upper()}] Spectral radius ρ(A) = {rho:.4f} (must be < 1)"
)
```
## Model Variants
Pre-configured scales from 1B to 1T parameters:
```python
from open_mythos import (
mythos_1b,
mythos_3b,
mythos_10b,
mythos_50b,
mythos_100b,
mythos_500b,
mythos_1t,
OpenMythos,
)
cfg = mythos_7b() # returns a MythosConfig
model = OpeExcerpt of 22,060 characters
Read on GitHub
Kye Gomez · Swarms · United States
38
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:48127a7dbf85ffc6, topic:pytorch, topic:jax